Informatics Publishing Limited

S. M. Zakariya ¹, Manzoor A. Lone ², Rashid Ali ¹

Affiliations
1 Department of Computer Engineering Zakir Hussain College of Engineering and Technology, AMU, Aligarh, IN
2 Department of Computer Science & Engineering Baba Ghulam Shah Badshah University, Rajouri, J&K, IN

Abstract

For the purpose of human authentication, the face recognition system is use as a biometric mode. As we know the face recognition is a technique of recognizing similar faces from face databases. It is the problem of searching a face in reference database to find the matches as a given face. The purpose is to find a face that has highest similarity with a given face in the database. The objective of face recognition involves the extraction of different features of the human face from the face image for discriminating it from other persons. Many face recognition algorithms have been developed and used as an application of access control and surveillance. For enhancing the performance and accuracy of biometric face recognition system, we use a multi-algorithmic approach, where in a combination of three different individual face recognition techniques is used. Recently, we developed six face recognition systems based on the six combinations of four individual techniques of face recognition system by fusing the scores of two approaches in a single face recognition system. In this paper, we develop four different face recognition systems based on the combinations of four individual techniques namely Principal Component Analysis (PCA), Discrete Cosine Transform (DCT), Template Matching using Correlation and Partitioned Iterative Function System (PIFS). We fuse the scores of three of these four techniques in a single face recognition system. We perform a comparative study of face recognition rate of these face recognition systems at two precision levels namely at top- 5 and at top-10. We experiment with a standard database called ORL face database. Experimentally, we find that each of these four systems perform well in comparison to the corresponding (in group of two) six combinations of four individual techniques. Overall, the system based on combination of PCA, DCT and Template Matching using Correlation is giving the best performance among these four systems.

Keywords

A Face Recognition System, PCA, DCT, Template Matching using Correlation, PIFS, Multi-Algorithmic Techniques of Six Systems, ORL Face Database and Face Recognition Rate.

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